Journal of Sports Analytics

In this manuscript, different approaches for modeling and prediction of tennis matches in Grand Slam tournaments are proposed. The data used here contain information on 5,013 matches in men’s Grand Slam tournaments from the years 2011–2022. All regarded approaches are based on regression models, modeling the probability of the first-named player winning. Several potential covariates are considered including the players’ age, the ATP ranking and points, odds, elo rating as well as two additional age variables, which take into account that the optimal age of a tennis player is between 28 and 32 years. We compare the different regression model approaches with respect to three performance measures, namely classification rate, predictive Bernoulli likelihood, and Brier score in a 43-fold cross-validation-type approach for the matches of the years 2011 to 2021. The top five optimal models with highest average ranks are then selected. In order to predict and compare the results of the tournaments in 2022 with the actual results, a comparison over a continuously updating data set via a “rolling window” strategy is used. Also, again the previously mentioned performance measures are calculated. Additionally, we examine whether the assumption of non-linear effects or additional court- and player-specific abilities is reasonable.

The aim of the study was to determine whether there are differences in performance analysis in handball between Pre-COVID-19, during COVID-19, and Post-COVID-19 seasons. The study material was obtained from the official match statistics of PGNiG Super league Ltd. Matches were played in the 2019/2020 season before COVID-19, 2020/2021 during COVID-19, and 2021/2022 Post-COVID-19. The Mann-Whitney U test was used for comparisons between two groups, for three groups using the Kruskal-Wallis test. In Pre-COVID-19 season, players made an average of 1.3 more 9 meter throws the Post-COVID-19. Post-COVID-19 season is characterized by a higher 6 meter goals and 6 meter throw count with respect to the Pre-COVID-19. The results show a higher goalkeeper 7 meter throw effectiveness in Pre-COVID-19 season than in COVID-19. The increasing number of throws and goals from the 6th meter along with a decrease in the number of throws from the 9th meter indicates the latest trends in handball. A reduction in the number of offensive fouls and an increase in the number of fast attacks and the effectiveness of goalkeepers’ interventions from 7 meters in the second round of the COVID-19 season indicates the adaptation of players to the new conditions created by the pandemic.

From the dawn of the “Moneyball” system of searching for players with undervalued skills, an increasing proportion of players chosen in the Major League draft has come from the collegiate ranks, and while every professional team has an analytics department, the draft remains the last frontier for identifying and acquiring the best prospective players. Thus, it has become more important in recent years to evaluate college players properly, and while players’ statistics during the college season can vary wildly due to differing levels of competition, it is necessary to find a more objective metric for measuring college players’ skills.

We propose that the most effective metric for doing so comes from observing players’ performances during the summer, when the variable of strength of schedule can be directly controlled, as players of the same skill level compete against each other. Our study focuses on the Cape Cod Baseball League (CCBL), a prestigious summer league that attracts the most talented college players, from which many players are drafted into the Majors every year. Our reasons for choosing the CCBL are the aforementioned homogeneity of talent; the lack of effects of travel fatigue, as the teams all play in a concentrated geographical area; and the league’s built-in replacement level, as temporary players often fill roster spots for players who had been selected the previous autumn, but whose college teams have advanced to the College World Series or who play on a national team during part of the CCBL season. This replacement level is used to calculate a metric of Wins Above Replacement, which we call cWAR. 1

 In sports, an aging curve depicts the relationship between average performance and age in athletes’ careers. This paper investigates the aging curves for offensive players in Major League Baseball. We study this problem in a missing data context and account for different types of dropouts of baseball players during their careers. We employ a multiple imputation framework for multilevel data to impute the player performance associated with the missing seasons, and estimate the aging curves based on the imputed datasets. We then evaluate the effects of different dropout mechanisms on the aging curves through simulation, before applying our method to analyze MLB player data from past seasons. Results suggest an overestimation of the aging curves constructed without considering the unobserved seasons, whereas estimates obtained from multiple imputation address this shortcoming.

Ranking countries in the Olympic Games by medal counts clearly favors large-population countries over small ones, while ranking by medals-per-capita produces national rankings with very small population countries on top. We discuss why this happens, and propose a new national ranking system for the Olympics, also based upon medals won, which is inclusive in the sense that countries of widely-varying population can achieve high rankings. This population-adjusted probability ranking ranks countries by how much evidence they show for high capability at Olympic sports. In particular, it ranks countries according to how improbable their medal counts would be in an idealized reference model of the Games which posits that all medal-winning nations have equal propensity per capita for winning medals. The ranking index U is defined using a simple binomial sum. Here we explain the method, and we present population-adjusted national rankings for the last three summer Olympics (London 2012, Rio 2016 and Tokyo 2020, held in 2021). If the advantages of this ranking method come to be understood by sports media covering the Olympics and by the interested public, it could be widely reported alongside raw medal counts, thus adding excitement and interest to the Olympics.

In the literature, information on the rally length distribution is quite incomplete, fragmented and non-homogeneous. In this paper we fill the gap deeply analyzing the distribution of rally length in professional tennis matches in the following directions: i) we provide the empirical distribution of the rally length, not only for some categories, but for each single length; ii) we consider different distributions for men and women and for different surfaces; iii) we find the statistical distribution best fitting the data for each surface; iv) we show how the rally distribution depends on some variables, such as the probabilities of winning a point at serve and players’ heights; v) previous points are based on a much larger sample size than other works leading to very reliable results. Our analyses point out that the best distribution for rally length is a zero-one-modified Geometric distribution, whose parameters are functions of the probabilities of winning a point at serve and of the players’ heights. Results suggest that the the players’ heights is the most impacting variable on the rally length distribution.

In sports, players usually undergo pressure situations in order to meet certain performance standards set by themselves or others. During the course of any competitive sporting event, players are often motivated to take calculated risks in attempts to secure a victory or to showcase their performance capabilities. However, quantifying the pressure experienced by players at any given stage of a game is quite a challenging and imposing task. This study discusses a method to compute the pressure experienced by batters when pursuing a target in a T20I cricket match. Diligent and extensive exploration of psychological pressures motivates us to formulate a differential equation, based on a set of reasonable assumptions, whose solution gives rise to a useful pressure index formula for quantifying the level of pressure experienced by the batters of the team batting second, i.e., the team chasing the target. This index takes into account two key assumptions: first, the rate of change of pressure on the batters at a given stage of the game depends on the pressure at that stage; and, second, the required run rate commensurate with available resources remaining at that stage of the game. The available data are used to fine-tune the parameter of the model. The applicability of the proposed pressure index is illustrated using data for T20I matches played by the major test playing countries during the years 2017, 2018, 2019, and 2021. Additionally, this pressure index can be used not only to predict the probability of winning at any given stage of the second innings, but also to quantify the contribution of each batter, taking into account the pressure situation in which they score runs.

This paper examines the effect of carbon plated “super shoes” on the performance of elite male marathoners. Prototypes of these carbon plated shoes with extraordinary cushioning were used beginning in 2016, and became widely available in 2017. In order to quantify the effect of these shoes on elite athletes, we analyzed data on the number of athletes who ran a marathon in under 2 : 08 : 00 each year from 1985 through 2021. A multiple linear regression model was constructed that controlled for the non-“super shoe” related upward time trend in the number of sub-2 : 08 times, utilizing a Cochrane-Orcutt transformation to correct for autocorrelation. This model shows that this new shoe technology is responsible for an additional roughly 24 additional sub-2 : 08 times per year. Estimated from this, we find a shoe-related time reduction of 1 minute and 31 seconds, or a 1.174% decrease in time.